Sparse classification of hyperspectral image based on first-order neighborhood system weighted constraint

Jiahui Liu; Hui Guan; Jiaojiao Li; Yunsong Li

doi:10.1117/12.2052990

Translator Disclaimer

22 May 2014 Sparse classification of hyperspectral image based on first-order neighborhood system weighted constraint

Jiahui Liu, Hui Guan, Jiaojiao Li, Yunsong Li

Author Affiliations +

Proceedings Volume 9124, Satellite Data Compression, Communications, and Processing X; 91240Z (2014) https://doi.org/10.1117/12.2052990
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, Maryland, United States

Abstract

In hyperspectral image classification, each hyperspectral pixel can be represented by linear combination of a few training samples in the training dictionary. Assuming the training dictionary is available, the hyperspectral pixel can be recovered using a minimal training samples by solving a sparse representation problem, then the weighted coefficients of training samples are obtained and the class of the pixel can be determined, the above process is called classification algorithm based on sparse representation. However, the traditional sparse classification algorithms have not fully utilized the spatial information and classification accuracy is relatively low. In this paper, in order to improve classification accuracy, a new sparse classification algorithm based on First-Order Neighborhood System Weighted (FONSW) constraint is proposed. Compared with other sparse classification algorithms, the experimental results show that the proposed algorithm has a smoother classification map and higher classification accuracy.

Citation Download Citation

Jiahui Liu, Hui Guan, Jiaojiao Li, and Yunsong Li "Sparse classification of hyperspectral image based on first-order neighborhood system weighted constraint", Proc. SPIE 9124, Satellite Data Compression, Communications, and Processing X, 91240Z (22 May 2014); https://doi.org/10.1117/12.2052990

ACCESS THE FULL ARTICLE